Single-Cell RNA-Seq with Bioconductor in R
Analyze single-cell RNA-Seq data using normalization, dimensionality reduction, clustering and differential expression.
- 4 hours
- 12 Videos
- 50 Exercises
- 4,434 Participants
- 4,100 XP
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Course Description
Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecedented opportunity to investigate fundamental biological questions at the cellular level, such as stem cell differentiation or the discovery and characterization of rare cell types. The majority of the computational methods to analyze single-cell RNA-Seq data are implemented in R making it a natural tool to start working with single-cell transcriptomic data. Using real single-cell datasets, this course provides a step-by-step tutorial to the methodology and associated R packages for the following four main tasks: (1) normalization, (2) dimensionality reduction, (3) clustering, (4) differential expression analysis.
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1
What is Single-Cell RNA-Seq?
FreeIn Chapter 1, you will learn what single-cell RNA-Seq is and why it is a such a powerful technique. By the end of this chapter, you'll also know how to load, create, and access single-cell datasets in R.
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Visualization and Dimensionality Reduction
When studying single-cell data at the cellular level, the number of dimensions is the number of genes. The goal of dimensionality reduction is to reduce the number of dimensions to a smaller number either to visualize the data in 2 dimensions or to prepare the dataset for subsequent steps like clustering.
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Quality Control and Normalization
In Chapter 2, we go over the first steps of the workflow to analyze single-cell RNA-seq data, which include quality control and normalization. These two steps should get all the technical issues and biases out of the way so that in the next chapters we can focus on the biological signal of interest.
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Cell Clustering and Differential expression analysis
In Chapter 4, we cluster cells with similar gene expression profiles and then perform differential expression (DE) analysis to find genes differentially expressed between known groups of cells. We then visualize DE genes with volcano plots and heatmaps.
-
1
What is Single-Cell RNA-Seq?
FreeIn Chapter 1, you will learn what single-cell RNA-Seq is and why it is a such a powerful technique. By the end of this chapter, you'll also know how to load, create, and access single-cell datasets in R.
-
Quality Control and Normalization
In Chapter 2, we go over the first steps of the workflow to analyze single-cell RNA-seq data, which include quality control and normalization. These two steps should get all the technical issues and biases out of the way so that in the next chapters we can focus on the biological signal of interest.
-
Visualization and Dimensionality Reduction
When studying single-cell data at the cellular level, the number of dimensions is the number of genes. The goal of dimensionality reduction is to reduce the number of dimensions to a smaller number either to visualize the data in 2 dimensions or to prepare the dataset for subsequent steps like clustering.
-
Cell Clustering and Differential expression analysis
In Chapter 4, we cluster cells with similar gene expression profiles and then perform differential expression (DE) analysis to find genes differentially expressed between known groups of cells. We then visualize DE genes with volcano plots and heatmaps.
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Fanny Perraudeau
Senior Data Scientist, Whole Biome
Fanny Perraudeau is a Senior Data Scientist at Whole Biome where she manages, designs, and implements novel genomics algorithms and bioinformatics pipelines to further improve the analysis of of Whole Biome microbiome data. In addition, she runs statistical analyses to aid the company’s therapeutic discovery efforts. She has a master from Ecole Polytechnique, France and a PhD in Biostatistics from University of California, Berkeley with a Designated Emphasis in Computational and Genomic Biology. Much of her work is motivated by the development and application of statistical methods and software for the analysis of biomedical and genomic data, especially metagenomics and single-cell RNASeq.
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Collaborators
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David Campos
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Shon Inouye
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Richie Cotton